{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/devamsheth21/Bearing-Fault-Detection-using-Deep-Learning-approach/blob/main/Training%20and%20Testing%20SDP%20images%20using%20CNN%20model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8JX3wg3zunF",
        "outputId": "10ff2a78-37fa-4148-bea3-2186c708e91f"
      },
      "outputs": [],
      "source": [
        "# from google.colab import drive\n",
        "\n",
        "# drive.mount(\"/content/gdrive\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cILVeGyEF4eW",
        "outputId": "9b5c8450-fda4-450e-ab53-235b55676ca7"
      },
      "outputs": [],
      "source": [
        "# !pip install -q efficientnet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YgmLK6Ug7FNI"
      },
      "outputs": [],
      "source": [
        "# '''!pip install split_folders\n",
        "# import splitfolders\n",
        "# [35,2],[40,2],[30,9],[50,2],[30,7],\n",
        "# #ztlist = [[35,9],[35,7],[60,2],[40,7],[40,9],[60,1],[30,2]] \n",
        "# ztlist =[(39, 3), (41, 6), (39, 5), (17, 2), (17, 3), (29, 4), (46, 6), (14, 4), (43, 5), (31, 4), (10, 7), (12, 3), (42, 2), (56, 8), (49, 2), (59, 3), (49, 4),(51, 4), (49, 10), (47, 8), (15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)]\n",
        "\n",
        "\n",
        "\n",
        "# for item in ztlist:\n",
        "\n",
        "#   input_folder = \"/content/gdrive/MyDrive/MP(Inner-Outer)/48kpart2/\" + \"z=\" + str(item[0])+\", \"+\"t=\" + str(item[1])\n",
        "\n",
        "#   output =\"/content/gdrive/MyDrive/MP(I-0)split/48kpart2/\" + \"z=\" + str(item[0])+\", \"+\"t=\" + str(item[1])\n",
        "  \n",
        "#   splitfolders.ratio(input_folder, output, seed=42, ratio=(.8, .1, .1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "nspC8kcnnI0o"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Copying files: 1800 files [00:00, 4259.52 files/s]\n",
            "Copying files: 1800 files [00:00, 5340.34 files/s]\n",
            "Copying files: 1800 files [00:00, 5259.53 files/s]\n",
            "Copying files: 1800 files [00:00, 5461.76 files/s]\n",
            "Copying files: 1800 files [00:00, 4698.57 files/s]\n",
            "Copying files: 1800 files [00:00, 5515.03 files/s]\n"
          ]
        }
      ],
      "source": [
        "#Pranav Run 1\n",
        "\n",
        "# !pip install split_folders\n",
        "import splitfolders\n",
        "# Merko bhulna nahi [35,2],[40,2],[30,9],[50,2],[30,7],\n",
        "#ztlist = [[35,9],[35,7],[60,2],[40,7],[40,9],[60,1],[30,2]] \n",
        "ztlist =[(15, 7), (13, 4), (34, 2), (29, 3), (40, 4), (12, 8)]\n",
        "\n",
        "for item in ztlist:\n",
        "\n",
        "  input_folder = \"./48kpart2/\"\n",
        " \n",
        "  output =\"./MP(I-0)split/48kpart2/\"\n",
        "  \n",
        "  splitfolders.ratio(input_folder, output, seed=42, ratio=(.8, .1, .1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "R2UlWvm-CYgo"
      },
      "outputs": [],
      "source": [
        "train_dir = './MP(I-0)split/48kpart2/train'\n",
        "val_dir   = './MP(I-0)split/48kpart2/val'\n",
        "test_dir  = './MP(I-0)split/48kpart2/test'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "PPrbq5GFCsIX"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "train_inner_fnames = os.listdir(train_dir+'/Inner' )\n",
        "train_outer_fnames = os.listdir(train_dir+'/Outer')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 796
        },
        "id": "ZmT5Ke8eDfVO",
        "outputId": "e6490d81-aa68-4b62-a978-118cb63903b3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x1600 with 16 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "# Parameters for our graph; we'll output images in a 10x10 configuration\n",
        "nrows = 4\n",
        "ncols = 4\n",
        "\n",
        "# Index for iterating over images\n",
        "pic_index = 0\n",
        "\n",
        "# Set up matplotlib fig, and size it to fit 4x4 pics\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(ncols * 4, nrows * 4)\n",
        "\n",
        "pic_index += 8\n",
        "next_outer_pix = [os.path.join(train_dir+'/Outer', fname) \n",
        "                for fname in train_outer_fnames[pic_index-8:pic_index]]\n",
        "next_inner_pix = [os.path.join(train_dir+'/Inner', fname) \n",
        "                for fname in train_inner_fnames[pic_index-8:pic_index]]\n",
        "\n",
        "\n",
        "for i, img_path in enumerate(next_outer_pix+next_inner_pix):\n",
        "  # Set up subplot; subplot indices start at 1\n",
        "  sp = plt.subplot(nrows, ncols, i + 1)\n",
        "  sp.axis('Off') # Don't show axes (or gridlines)\n",
        "\n",
        "  img = mpimg.imread(img_path)\n",
        "  plt.imshow(img)\n",
        "  \n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "URsomVbAEeEb"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "target_size=(288,432)\n",
        "batch_size = 16"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9V1Qw1PrEiv0",
        "outputId": "b6d535c6-9fbe-45d3-ade9-112e67430da6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 1440 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=0,\n",
        "    width_shift_range=0,\n",
        "    height_shift_range=0,\n",
        "    shear_range=0,\n",
        "    zoom_range=0,\n",
        "    horizontal_flip=False,\n",
        "    vertical_flip=False)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=target_size,\n",
        "    batch_size=batch_size,\n",
        "    color_mode='rgb',    \n",
        "    shuffle=False,\n",
        "    seed=42,\n",
        "    class_mode='categorical')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2ulE040EzbK",
        "outputId": "7f252d69-afdc-4f60-f7ba-32eebca07000"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 180 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "val_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "val_generator = val_datagen.flow_from_directory(\n",
        "    val_dir,\n",
        "    target_size=target_size,\n",
        "    batch_size=batch_size,\n",
        "    color_mode='rgb',\n",
        "    shuffle=False,    \n",
        "    class_mode='categorical')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2s_00EEnE88E",
        "outputId": "0c2a3a0f-24c0-42c5-e0ad-f96214f9cb17"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 180 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=target_size,\n",
        "    batch_size=batch_size,\n",
        "    color_mode='rgb',\n",
        "    shuffle=False,     \n",
        "    class_mode=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "XR-zoV3pFAc6"
      },
      "outputs": [],
      "source": [
        "num_classes = 2\n",
        "input_shape = (288,432,3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb7_notop.h5\n",
            "258076736/258076736 [==============================] - 649s 3us/step\n"
          ]
        }
      ],
      "source": [
        "# import efficientnet.tfkeras as efn\n",
        "# 加载 EfficientNetB0\n",
        "efn = tf.keras.applications.EfficientNetB7( \n",
        "    include_top=False,  # 是否包含全连接层进行分类\n",
        "    weights='imagenet',  # 使用预训练的 ImageNet 权重\n",
        "    input_shape=input_shape,  # 输入图像的尺寸和通道数\n",
        "    pooling=None,  # 是否进行全局平均池化\n",
        "    classes=2  # 分类的类别数，默认为1000（ImageNet的类别数）））)\n",
        " )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7hYY0-4wFs46",
        "outputId": "0336d83e-6c98-4539-87a1-fe84f7c1d745"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_4 (InputLayer)        [(None, 288, 432, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " rescaling_6 (Rescaling)     (None, 288, 432, 3)          0         ['input_4[0][0]']             \n",
            "                                                                                                  \n",
            " normalization_3 (Normaliza  (None, 288, 432, 3)          7         ['rescaling_6[0][0]']         \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " rescaling_7 (Rescaling)     (None, 288, 432, 3)          0         ['normalization_3[0][0]']     \n",
            "                                                                                                  \n",
            " stem_conv_pad (ZeroPadding  (None, 289, 433, 3)          0         ['rescaling_7[0][0]']         \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " stem_conv (Conv2D)          (None, 144, 216, 64)         1728      ['stem_conv_pad[0][0]']       \n",
            "                                                                                                  \n",
            " stem_bn (BatchNormalizatio  (None, 144, 216, 64)         256       ['stem_conv[0][0]']           \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " stem_activation (Activatio  (None, 144, 216, 64)         0         ['stem_bn[0][0]']             \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " block1a_dwconv (DepthwiseC  (None, 144, 216, 64)         576       ['stem_activation[0][0]']     \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block1a_bn (BatchNormaliza  (None, 144, 216, 64)         256       ['block1a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block1a_activation (Activa  (None, 144, 216, 64)         0         ['block1a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block1a_se_squeeze (Global  (None, 64)                   0         ['block1a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block1a_se_reshape (Reshap  (None, 1, 1, 64)             0         ['block1a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block1a_se_reduce (Conv2D)  (None, 1, 1, 16)             1040      ['block1a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block1a_se_expand (Conv2D)  (None, 1, 1, 64)             1088      ['block1a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block1a_se_excite (Multipl  (None, 144, 216, 64)         0         ['block1a_activation[0][0]',  \n",
            " y)                                                                  'block1a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block1a_project_conv (Conv  (None, 144, 216, 32)         2048      ['block1a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block1a_project_bn (BatchN  (None, 144, 216, 32)         128       ['block1a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block1b_dwconv (DepthwiseC  (None, 144, 216, 32)         288       ['block1a_project_bn[0][0]']  \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block1b_bn (BatchNormaliza  (None, 144, 216, 32)         128       ['block1b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block1b_activation (Activa  (None, 144, 216, 32)         0         ['block1b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block1b_se_squeeze (Global  (None, 32)                   0         ['block1b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block1b_se_reshape (Reshap  (None, 1, 1, 32)             0         ['block1b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block1b_se_reduce (Conv2D)  (None, 1, 1, 8)              264       ['block1b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block1b_se_expand (Conv2D)  (None, 1, 1, 32)             288       ['block1b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block1b_se_excite (Multipl  (None, 144, 216, 32)         0         ['block1b_activation[0][0]',  \n",
            " y)                                                                  'block1b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block1b_project_conv (Conv  (None, 144, 216, 32)         1024      ['block1b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block1b_project_bn (BatchN  (None, 144, 216, 32)         128       ['block1b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block1b_drop (Dropout)      (None, 144, 216, 32)         0         ['block1b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block1b_add (Add)           (None, 144, 216, 32)         0         ['block1b_drop[0][0]',        \n",
            "                                                                     'block1a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block1c_dwconv (DepthwiseC  (None, 144, 216, 32)         288       ['block1b_add[0][0]']         \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block1c_bn (BatchNormaliza  (None, 144, 216, 32)         128       ['block1c_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block1c_activation (Activa  (None, 144, 216, 32)         0         ['block1c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block1c_se_squeeze (Global  (None, 32)                   0         ['block1c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block1c_se_reshape (Reshap  (None, 1, 1, 32)             0         ['block1c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block1c_se_reduce (Conv2D)  (None, 1, 1, 8)              264       ['block1c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block1c_se_expand (Conv2D)  (None, 1, 1, 32)             288       ['block1c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block1c_se_excite (Multipl  (None, 144, 216, 32)         0         ['block1c_activation[0][0]',  \n",
            " y)                                                                  'block1c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block1c_project_conv (Conv  (None, 144, 216, 32)         1024      ['block1c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block1c_project_bn (BatchN  (None, 144, 216, 32)         128       ['block1c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block1c_drop (Dropout)      (None, 144, 216, 32)         0         ['block1c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block1c_add (Add)           (None, 144, 216, 32)         0         ['block1c_drop[0][0]',        \n",
            "                                                                     'block1b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block1d_dwconv (DepthwiseC  (None, 144, 216, 32)         288       ['block1c_add[0][0]']         \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block1d_bn (BatchNormaliza  (None, 144, 216, 32)         128       ['block1d_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block1d_activation (Activa  (None, 144, 216, 32)         0         ['block1d_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block1d_se_squeeze (Global  (None, 32)                   0         ['block1d_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block1d_se_reshape (Reshap  (None, 1, 1, 32)             0         ['block1d_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block1d_se_reduce (Conv2D)  (None, 1, 1, 8)              264       ['block1d_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block1d_se_expand (Conv2D)  (None, 1, 1, 32)             288       ['block1d_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block1d_se_excite (Multipl  (None, 144, 216, 32)         0         ['block1d_activation[0][0]',  \n",
            " y)                                                                  'block1d_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block1d_project_conv (Conv  (None, 144, 216, 32)         1024      ['block1d_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block1d_project_bn (BatchN  (None, 144, 216, 32)         128       ['block1d_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block1d_drop (Dropout)      (None, 144, 216, 32)         0         ['block1d_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block1d_add (Add)           (None, 144, 216, 32)         0         ['block1d_drop[0][0]',        \n",
            "                                                                     'block1c_add[0][0]']         \n",
            "                                                                                                  \n",
            " block2a_expand_conv (Conv2  (None, 144, 216, 192)        6144      ['block1d_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2a_expand_bn (BatchNo  (None, 144, 216, 192)        768       ['block2a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2a_expand_activation   (None, 144, 216, 192)        0         ['block2a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2a_dwconv_pad (ZeroPa  (None, 145, 217, 192)        0         ['block2a_expand_activation[0]\n",
            " dding2D)                                                           [0]']                         \n",
            "                                                                                                  \n",
            " block2a_dwconv (DepthwiseC  (None, 72, 108, 192)         1728      ['block2a_dwconv_pad[0][0]']  \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block2a_bn (BatchNormaliza  (None, 72, 108, 192)         768       ['block2a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2a_activation (Activa  (None, 72, 108, 192)         0         ['block2a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2a_se_squeeze (Global  (None, 192)                  0         ['block2a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block2a_se_reshape (Reshap  (None, 1, 1, 192)            0         ['block2a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block2a_se_reduce (Conv2D)  (None, 1, 1, 8)              1544      ['block2a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block2a_se_expand (Conv2D)  (None, 1, 1, 192)            1728      ['block2a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block2a_se_excite (Multipl  (None, 72, 108, 192)         0         ['block2a_activation[0][0]',  \n",
            " y)                                                                  'block2a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block2a_project_conv (Conv  (None, 72, 108, 48)          9216      ['block2a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block2a_project_bn (BatchN  (None, 72, 108, 48)          192       ['block2a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2b_expand_conv (Conv2  (None, 72, 108, 288)         13824     ['block2a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2b_expand_bn (BatchNo  (None, 72, 108, 288)         1152      ['block2b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2b_expand_activation   (None, 72, 108, 288)         0         ['block2b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2b_dwconv (DepthwiseC  (None, 72, 108, 288)         2592      ['block2b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block2b_bn (BatchNormaliza  (None, 72, 108, 288)         1152      ['block2b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2b_activation (Activa  (None, 72, 108, 288)         0         ['block2b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2b_se_squeeze (Global  (None, 288)                  0         ['block2b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block2b_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block2b_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block2b_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block2b_se_excite (Multipl  (None, 72, 108, 288)         0         ['block2b_activation[0][0]',  \n",
            " y)                                                                  'block2b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block2b_project_conv (Conv  (None, 72, 108, 48)          13824     ['block2b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block2b_project_bn (BatchN  (None, 72, 108, 48)          192       ['block2b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2b_drop (Dropout)      (None, 72, 108, 48)          0         ['block2b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block2b_add (Add)           (None, 72, 108, 48)          0         ['block2b_drop[0][0]',        \n",
            "                                                                     'block2a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block2c_expand_conv (Conv2  (None, 72, 108, 288)         13824     ['block2b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2c_expand_bn (BatchNo  (None, 72, 108, 288)         1152      ['block2c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2c_expand_activation   (None, 72, 108, 288)         0         ['block2c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2c_dwconv (DepthwiseC  (None, 72, 108, 288)         2592      ['block2c_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block2c_bn (BatchNormaliza  (None, 72, 108, 288)         1152      ['block2c_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2c_activation (Activa  (None, 72, 108, 288)         0         ['block2c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2c_se_squeeze (Global  (None, 288)                  0         ['block2c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block2c_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block2c_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block2c_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block2c_se_excite (Multipl  (None, 72, 108, 288)         0         ['block2c_activation[0][0]',  \n",
            " y)                                                                  'block2c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block2c_project_conv (Conv  (None, 72, 108, 48)          13824     ['block2c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block2c_project_bn (BatchN  (None, 72, 108, 48)          192       ['block2c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2c_drop (Dropout)      (None, 72, 108, 48)          0         ['block2c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block2c_add (Add)           (None, 72, 108, 48)          0         ['block2c_drop[0][0]',        \n",
            "                                                                     'block2b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block2d_expand_conv (Conv2  (None, 72, 108, 288)         13824     ['block2c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2d_expand_bn (BatchNo  (None, 72, 108, 288)         1152      ['block2d_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2d_expand_activation   (None, 72, 108, 288)         0         ['block2d_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2d_dwconv (DepthwiseC  (None, 72, 108, 288)         2592      ['block2d_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block2d_bn (BatchNormaliza  (None, 72, 108, 288)         1152      ['block2d_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2d_activation (Activa  (None, 72, 108, 288)         0         ['block2d_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2d_se_squeeze (Global  (None, 288)                  0         ['block2d_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block2d_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2d_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block2d_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2d_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block2d_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2d_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block2d_se_excite (Multipl  (None, 72, 108, 288)         0         ['block2d_activation[0][0]',  \n",
            " y)                                                                  'block2d_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block2d_project_conv (Conv  (None, 72, 108, 48)          13824     ['block2d_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block2d_project_bn (BatchN  (None, 72, 108, 48)          192       ['block2d_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2d_drop (Dropout)      (None, 72, 108, 48)          0         ['block2d_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block2d_add (Add)           (None, 72, 108, 48)          0         ['block2d_drop[0][0]',        \n",
            "                                                                     'block2c_add[0][0]']         \n",
            "                                                                                                  \n",
            " block2e_expand_conv (Conv2  (None, 72, 108, 288)         13824     ['block2d_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2e_expand_bn (BatchNo  (None, 72, 108, 288)         1152      ['block2e_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2e_expand_activation   (None, 72, 108, 288)         0         ['block2e_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2e_dwconv (DepthwiseC  (None, 72, 108, 288)         2592      ['block2e_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block2e_bn (BatchNormaliza  (None, 72, 108, 288)         1152      ['block2e_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2e_activation (Activa  (None, 72, 108, 288)         0         ['block2e_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2e_se_squeeze (Global  (None, 288)                  0         ['block2e_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block2e_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2e_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block2e_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2e_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block2e_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2e_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block2e_se_excite (Multipl  (None, 72, 108, 288)         0         ['block2e_activation[0][0]',  \n",
            " y)                                                                  'block2e_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block2e_project_conv (Conv  (None, 72, 108, 48)          13824     ['block2e_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block2e_project_bn (BatchN  (None, 72, 108, 48)          192       ['block2e_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2e_drop (Dropout)      (None, 72, 108, 48)          0         ['block2e_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block2e_add (Add)           (None, 72, 108, 48)          0         ['block2e_drop[0][0]',        \n",
            "                                                                     'block2d_add[0][0]']         \n",
            "                                                                                                  \n",
            " block2f_expand_conv (Conv2  (None, 72, 108, 288)         13824     ['block2e_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2f_expand_bn (BatchNo  (None, 72, 108, 288)         1152      ['block2f_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2f_expand_activation   (None, 72, 108, 288)         0         ['block2f_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2f_dwconv (DepthwiseC  (None, 72, 108, 288)         2592      ['block2f_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block2f_bn (BatchNormaliza  (None, 72, 108, 288)         1152      ['block2f_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2f_activation (Activa  (None, 72, 108, 288)         0         ['block2f_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2f_se_squeeze (Global  (None, 288)                  0         ['block2f_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block2f_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2f_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block2f_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2f_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block2f_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2f_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block2f_se_excite (Multipl  (None, 72, 108, 288)         0         ['block2f_activation[0][0]',  \n",
            " y)                                                                  'block2f_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block2f_project_conv (Conv  (None, 72, 108, 48)          13824     ['block2f_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block2f_project_bn (BatchN  (None, 72, 108, 48)          192       ['block2f_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2f_drop (Dropout)      (None, 72, 108, 48)          0         ['block2f_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block2f_add (Add)           (None, 72, 108, 48)          0         ['block2f_drop[0][0]',        \n",
            "                                                                     'block2e_add[0][0]']         \n",
            "                                                                                                  \n",
            " block2g_expand_conv (Conv2  (None, 72, 108, 288)         13824     ['block2f_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2g_expand_bn (BatchNo  (None, 72, 108, 288)         1152      ['block2g_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2g_expand_activation   (None, 72, 108, 288)         0         ['block2g_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2g_dwconv (DepthwiseC  (None, 72, 108, 288)         2592      ['block2g_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block2g_bn (BatchNormaliza  (None, 72, 108, 288)         1152      ['block2g_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2g_activation (Activa  (None, 72, 108, 288)         0         ['block2g_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2g_se_squeeze (Global  (None, 288)                  0         ['block2g_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block2g_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block2g_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block2g_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block2g_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block2g_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block2g_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block2g_se_excite (Multipl  (None, 72, 108, 288)         0         ['block2g_activation[0][0]',  \n",
            " y)                                                                  'block2g_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block2g_project_conv (Conv  (None, 72, 108, 48)          13824     ['block2g_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block2g_project_bn (BatchN  (None, 72, 108, 48)          192       ['block2g_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2g_drop (Dropout)      (None, 72, 108, 48)          0         ['block2g_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block2g_add (Add)           (None, 72, 108, 48)          0         ['block2g_drop[0][0]',        \n",
            "                                                                     'block2f_add[0][0]']         \n",
            "                                                                                                  \n",
            " block3a_expand_conv (Conv2  (None, 72, 108, 288)         13824     ['block2g_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3a_expand_bn (BatchNo  (None, 72, 108, 288)         1152      ['block3a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3a_expand_activation   (None, 72, 108, 288)         0         ['block3a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3a_dwconv_pad (ZeroPa  (None, 75, 111, 288)         0         ['block3a_expand_activation[0]\n",
            " dding2D)                                                           [0]']                         \n",
            "                                                                                                  \n",
            " block3a_dwconv (DepthwiseC  (None, 36, 54, 288)          7200      ['block3a_dwconv_pad[0][0]']  \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block3a_bn (BatchNormaliza  (None, 36, 54, 288)          1152      ['block3a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3a_activation (Activa  (None, 36, 54, 288)          0         ['block3a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3a_se_squeeze (Global  (None, 288)                  0         ['block3a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block3a_se_reshape (Reshap  (None, 1, 1, 288)            0         ['block3a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block3a_se_reduce (Conv2D)  (None, 1, 1, 12)             3468      ['block3a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block3a_se_expand (Conv2D)  (None, 1, 1, 288)            3744      ['block3a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block3a_se_excite (Multipl  (None, 36, 54, 288)          0         ['block3a_activation[0][0]',  \n",
            " y)                                                                  'block3a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block3a_project_conv (Conv  (None, 36, 54, 80)           23040     ['block3a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block3a_project_bn (BatchN  (None, 36, 54, 80)           320       ['block3a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3b_expand_conv (Conv2  (None, 36, 54, 480)          38400     ['block3a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3b_expand_bn (BatchNo  (None, 36, 54, 480)          1920      ['block3b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3b_expand_activation   (None, 36, 54, 480)          0         ['block3b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3b_dwconv (DepthwiseC  (None, 36, 54, 480)          12000     ['block3b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block3b_bn (BatchNormaliza  (None, 36, 54, 480)          1920      ['block3b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3b_activation (Activa  (None, 36, 54, 480)          0         ['block3b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3b_se_squeeze (Global  (None, 480)                  0         ['block3b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block3b_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block3b_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block3b_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block3b_se_excite (Multipl  (None, 36, 54, 480)          0         ['block3b_activation[0][0]',  \n",
            " y)                                                                  'block3b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block3b_project_conv (Conv  (None, 36, 54, 80)           38400     ['block3b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block3b_project_bn (BatchN  (None, 36, 54, 80)           320       ['block3b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3b_drop (Dropout)      (None, 36, 54, 80)           0         ['block3b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3b_add (Add)           (None, 36, 54, 80)           0         ['block3b_drop[0][0]',        \n",
            "                                                                     'block3a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3c_expand_conv (Conv2  (None, 36, 54, 480)          38400     ['block3b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3c_expand_bn (BatchNo  (None, 36, 54, 480)          1920      ['block3c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3c_expand_activation   (None, 36, 54, 480)          0         ['block3c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3c_dwconv (DepthwiseC  (None, 36, 54, 480)          12000     ['block3c_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block3c_bn (BatchNormaliza  (None, 36, 54, 480)          1920      ['block3c_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3c_activation (Activa  (None, 36, 54, 480)          0         ['block3c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3c_se_squeeze (Global  (None, 480)                  0         ['block3c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block3c_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block3c_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block3c_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block3c_se_excite (Multipl  (None, 36, 54, 480)          0         ['block3c_activation[0][0]',  \n",
            " y)                                                                  'block3c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block3c_project_conv (Conv  (None, 36, 54, 80)           38400     ['block3c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block3c_project_bn (BatchN  (None, 36, 54, 80)           320       ['block3c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3c_drop (Dropout)      (None, 36, 54, 80)           0         ['block3c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3c_add (Add)           (None, 36, 54, 80)           0         ['block3c_drop[0][0]',        \n",
            "                                                                     'block3b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block3d_expand_conv (Conv2  (None, 36, 54, 480)          38400     ['block3c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3d_expand_bn (BatchNo  (None, 36, 54, 480)          1920      ['block3d_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3d_expand_activation   (None, 36, 54, 480)          0         ['block3d_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3d_dwconv (DepthwiseC  (None, 36, 54, 480)          12000     ['block3d_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block3d_bn (BatchNormaliza  (None, 36, 54, 480)          1920      ['block3d_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3d_activation (Activa  (None, 36, 54, 480)          0         ['block3d_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3d_se_squeeze (Global  (None, 480)                  0         ['block3d_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block3d_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3d_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block3d_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3d_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block3d_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3d_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block3d_se_excite (Multipl  (None, 36, 54, 480)          0         ['block3d_activation[0][0]',  \n",
            " y)                                                                  'block3d_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block3d_project_conv (Conv  (None, 36, 54, 80)           38400     ['block3d_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block3d_project_bn (BatchN  (None, 36, 54, 80)           320       ['block3d_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3d_drop (Dropout)      (None, 36, 54, 80)           0         ['block3d_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3d_add (Add)           (None, 36, 54, 80)           0         ['block3d_drop[0][0]',        \n",
            "                                                                     'block3c_add[0][0]']         \n",
            "                                                                                                  \n",
            " block3e_expand_conv (Conv2  (None, 36, 54, 480)          38400     ['block3d_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3e_expand_bn (BatchNo  (None, 36, 54, 480)          1920      ['block3e_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3e_expand_activation   (None, 36, 54, 480)          0         ['block3e_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3e_dwconv (DepthwiseC  (None, 36, 54, 480)          12000     ['block3e_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block3e_bn (BatchNormaliza  (None, 36, 54, 480)          1920      ['block3e_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3e_activation (Activa  (None, 36, 54, 480)          0         ['block3e_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3e_se_squeeze (Global  (None, 480)                  0         ['block3e_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block3e_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3e_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block3e_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3e_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block3e_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3e_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block3e_se_excite (Multipl  (None, 36, 54, 480)          0         ['block3e_activation[0][0]',  \n",
            " y)                                                                  'block3e_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block3e_project_conv (Conv  (None, 36, 54, 80)           38400     ['block3e_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block3e_project_bn (BatchN  (None, 36, 54, 80)           320       ['block3e_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3e_drop (Dropout)      (None, 36, 54, 80)           0         ['block3e_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3e_add (Add)           (None, 36, 54, 80)           0         ['block3e_drop[0][0]',        \n",
            "                                                                     'block3d_add[0][0]']         \n",
            "                                                                                                  \n",
            " block3f_expand_conv (Conv2  (None, 36, 54, 480)          38400     ['block3e_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3f_expand_bn (BatchNo  (None, 36, 54, 480)          1920      ['block3f_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3f_expand_activation   (None, 36, 54, 480)          0         ['block3f_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3f_dwconv (DepthwiseC  (None, 36, 54, 480)          12000     ['block3f_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block3f_bn (BatchNormaliza  (None, 36, 54, 480)          1920      ['block3f_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3f_activation (Activa  (None, 36, 54, 480)          0         ['block3f_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3f_se_squeeze (Global  (None, 480)                  0         ['block3f_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block3f_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3f_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block3f_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3f_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block3f_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3f_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block3f_se_excite (Multipl  (None, 36, 54, 480)          0         ['block3f_activation[0][0]',  \n",
            " y)                                                                  'block3f_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block3f_project_conv (Conv  (None, 36, 54, 80)           38400     ['block3f_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block3f_project_bn (BatchN  (None, 36, 54, 80)           320       ['block3f_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3f_drop (Dropout)      (None, 36, 54, 80)           0         ['block3f_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3f_add (Add)           (None, 36, 54, 80)           0         ['block3f_drop[0][0]',        \n",
            "                                                                     'block3e_add[0][0]']         \n",
            "                                                                                                  \n",
            " block3g_expand_conv (Conv2  (None, 36, 54, 480)          38400     ['block3f_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3g_expand_bn (BatchNo  (None, 36, 54, 480)          1920      ['block3g_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3g_expand_activation   (None, 36, 54, 480)          0         ['block3g_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3g_dwconv (DepthwiseC  (None, 36, 54, 480)          12000     ['block3g_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block3g_bn (BatchNormaliza  (None, 36, 54, 480)          1920      ['block3g_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3g_activation (Activa  (None, 36, 54, 480)          0         ['block3g_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3g_se_squeeze (Global  (None, 480)                  0         ['block3g_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block3g_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block3g_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block3g_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block3g_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block3g_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block3g_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block3g_se_excite (Multipl  (None, 36, 54, 480)          0         ['block3g_activation[0][0]',  \n",
            " y)                                                                  'block3g_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block3g_project_conv (Conv  (None, 36, 54, 80)           38400     ['block3g_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block3g_project_bn (BatchN  (None, 36, 54, 80)           320       ['block3g_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3g_drop (Dropout)      (None, 36, 54, 80)           0         ['block3g_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3g_add (Add)           (None, 36, 54, 80)           0         ['block3g_drop[0][0]',        \n",
            "                                                                     'block3f_add[0][0]']         \n",
            "                                                                                                  \n",
            " block4a_expand_conv (Conv2  (None, 36, 54, 480)          38400     ['block3g_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4a_expand_bn (BatchNo  (None, 36, 54, 480)          1920      ['block4a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4a_expand_activation   (None, 36, 54, 480)          0         ['block4a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4a_dwconv_pad (ZeroPa  (None, 37, 55, 480)          0         ['block4a_expand_activation[0]\n",
            " dding2D)                                                           [0]']                         \n",
            "                                                                                                  \n",
            " block4a_dwconv (DepthwiseC  (None, 18, 27, 480)          4320      ['block4a_dwconv_pad[0][0]']  \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block4a_bn (BatchNormaliza  (None, 18, 27, 480)          1920      ['block4a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4a_activation (Activa  (None, 18, 27, 480)          0         ['block4a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4a_se_squeeze (Global  (None, 480)                  0         ['block4a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4a_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block4a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4a_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block4a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4a_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block4a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4a_se_excite (Multipl  (None, 18, 27, 480)          0         ['block4a_activation[0][0]',  \n",
            " y)                                                                  'block4a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4a_project_conv (Conv  (None, 18, 27, 160)          76800     ['block4a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4a_project_bn (BatchN  (None, 18, 27, 160)          640       ['block4a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_expand_conv (Conv2  (None, 18, 27, 960)          153600    ['block4a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4b_expand_bn (BatchNo  (None, 18, 27, 960)          3840      ['block4b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4b_expand_activation   (None, 18, 27, 960)          0         ['block4b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4b_dwconv (DepthwiseC  (None, 18, 27, 960)          8640      ['block4b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4b_bn (BatchNormaliza  (None, 18, 27, 960)          3840      ['block4b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4b_activation (Activa  (None, 18, 27, 960)          0         ['block4b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4b_se_squeeze (Global  (None, 960)                  0         ['block4b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4b_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4b_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4b_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4b_se_excite (Multipl  (None, 18, 27, 960)          0         ['block4b_activation[0][0]',  \n",
            " y)                                                                  'block4b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4b_project_conv (Conv  (None, 18, 27, 160)          153600    ['block4b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4b_project_bn (BatchN  (None, 18, 27, 160)          640       ['block4b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_drop (Dropout)      (None, 18, 27, 160)          0         ['block4b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4b_add (Add)           (None, 18, 27, 160)          0         ['block4b_drop[0][0]',        \n",
            "                                                                     'block4a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4c_expand_conv (Conv2  (None, 18, 27, 960)          153600    ['block4b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4c_expand_bn (BatchNo  (None, 18, 27, 960)          3840      ['block4c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4c_expand_activation   (None, 18, 27, 960)          0         ['block4c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4c_dwconv (DepthwiseC  (None, 18, 27, 960)          8640      ['block4c_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4c_bn (BatchNormaliza  (None, 18, 27, 960)          3840      ['block4c_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4c_activation (Activa  (None, 18, 27, 960)          0         ['block4c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4c_se_squeeze (Global  (None, 960)                  0         ['block4c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4c_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4c_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4c_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4c_se_excite (Multipl  (None, 18, 27, 960)          0         ['block4c_activation[0][0]',  \n",
            " y)                                                                  'block4c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4c_project_conv (Conv  (None, 18, 27, 160)          153600    ['block4c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4c_project_bn (BatchN  (None, 18, 27, 160)          640       ['block4c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4c_drop (Dropout)      (None, 18, 27, 160)          0         ['block4c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4c_add (Add)           (None, 18, 27, 160)          0         ['block4c_drop[0][0]',        \n",
            "                                                                     'block4b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block4d_expand_conv (Conv2  (None, 18, 27, 960)          153600    ['block4c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4d_expand_bn (BatchNo  (None, 18, 27, 960)          3840      ['block4d_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4d_expand_activation   (None, 18, 27, 960)          0         ['block4d_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4d_dwconv (DepthwiseC  (None, 18, 27, 960)          8640      ['block4d_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4d_bn (BatchNormaliza  (None, 18, 27, 960)          3840      ['block4d_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4d_activation (Activa  (None, 18, 27, 960)          0         ['block4d_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4d_se_squeeze (Global  (None, 960)                  0         ['block4d_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4d_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4d_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4d_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4d_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4d_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4d_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4d_se_excite (Multipl  (None, 18, 27, 960)          0         ['block4d_activation[0][0]',  \n",
            " y)                                                                  'block4d_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4d_project_conv (Conv  (None, 18, 27, 160)          153600    ['block4d_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4d_project_bn (BatchN  (None, 18, 27, 160)          640       ['block4d_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4d_drop (Dropout)      (None, 18, 27, 160)          0         ['block4d_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4d_add (Add)           (None, 18, 27, 160)          0         ['block4d_drop[0][0]',        \n",
            "                                                                     'block4c_add[0][0]']         \n",
            "                                                                                                  \n",
            " block4e_expand_conv (Conv2  (None, 18, 27, 960)          153600    ['block4d_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4e_expand_bn (BatchNo  (None, 18, 27, 960)          3840      ['block4e_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4e_expand_activation   (None, 18, 27, 960)          0         ['block4e_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4e_dwconv (DepthwiseC  (None, 18, 27, 960)          8640      ['block4e_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4e_bn (BatchNormaliza  (None, 18, 27, 960)          3840      ['block4e_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4e_activation (Activa  (None, 18, 27, 960)          0         ['block4e_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4e_se_squeeze (Global  (None, 960)                  0         ['block4e_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4e_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4e_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4e_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4e_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4e_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4e_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4e_se_excite (Multipl  (None, 18, 27, 960)          0         ['block4e_activation[0][0]',  \n",
            " y)                                                                  'block4e_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4e_project_conv (Conv  (None, 18, 27, 160)          153600    ['block4e_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4e_project_bn (BatchN  (None, 18, 27, 160)          640       ['block4e_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4e_drop (Dropout)      (None, 18, 27, 160)          0         ['block4e_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4e_add (Add)           (None, 18, 27, 160)          0         ['block4e_drop[0][0]',        \n",
            "                                                                     'block4d_add[0][0]']         \n",
            "                                                                                                  \n",
            " block4f_expand_conv (Conv2  (None, 18, 27, 960)          153600    ['block4e_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4f_expand_bn (BatchNo  (None, 18, 27, 960)          3840      ['block4f_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4f_expand_activation   (None, 18, 27, 960)          0         ['block4f_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4f_dwconv (DepthwiseC  (None, 18, 27, 960)          8640      ['block4f_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4f_bn (BatchNormaliza  (None, 18, 27, 960)          3840      ['block4f_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4f_activation (Activa  (None, 18, 27, 960)          0         ['block4f_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4f_se_squeeze (Global  (None, 960)                  0         ['block4f_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4f_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4f_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4f_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4f_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4f_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4f_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4f_se_excite (Multipl  (None, 18, 27, 960)          0         ['block4f_activation[0][0]',  \n",
            " y)                                                                  'block4f_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4f_project_conv (Conv  (None, 18, 27, 160)          153600    ['block4f_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4f_project_bn (BatchN  (None, 18, 27, 160)          640       ['block4f_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4f_drop (Dropout)      (None, 18, 27, 160)          0         ['block4f_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4f_add (Add)           (None, 18, 27, 160)          0         ['block4f_drop[0][0]',        \n",
            "                                                                     'block4e_add[0][0]']         \n",
            "                                                                                                  \n",
            " block4g_expand_conv (Conv2  (None, 18, 27, 960)          153600    ['block4f_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4g_expand_bn (BatchNo  (None, 18, 27, 960)          3840      ['block4g_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4g_expand_activation   (None, 18, 27, 960)          0         ['block4g_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4g_dwconv (DepthwiseC  (None, 18, 27, 960)          8640      ['block4g_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4g_bn (BatchNormaliza  (None, 18, 27, 960)          3840      ['block4g_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4g_activation (Activa  (None, 18, 27, 960)          0         ['block4g_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4g_se_squeeze (Global  (None, 960)                  0         ['block4g_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4g_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4g_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4g_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4g_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4g_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4g_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4g_se_excite (Multipl  (None, 18, 27, 960)          0         ['block4g_activation[0][0]',  \n",
            " y)                                                                  'block4g_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4g_project_conv (Conv  (None, 18, 27, 160)          153600    ['block4g_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4g_project_bn (BatchN  (None, 18, 27, 160)          640       ['block4g_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4g_drop (Dropout)      (None, 18, 27, 160)          0         ['block4g_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4g_add (Add)           (None, 18, 27, 160)          0         ['block4g_drop[0][0]',        \n",
            "                                                                     'block4f_add[0][0]']         \n",
            "                                                                                                  \n",
            " block4h_expand_conv (Conv2  (None, 18, 27, 960)          153600    ['block4g_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4h_expand_bn (BatchNo  (None, 18, 27, 960)          3840      ['block4h_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4h_expand_activation   (None, 18, 27, 960)          0         ['block4h_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4h_dwconv (DepthwiseC  (None, 18, 27, 960)          8640      ['block4h_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4h_bn (BatchNormaliza  (None, 18, 27, 960)          3840      ['block4h_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4h_activation (Activa  (None, 18, 27, 960)          0         ['block4h_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4h_se_squeeze (Global  (None, 960)                  0         ['block4h_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4h_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4h_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4h_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4h_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4h_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4h_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4h_se_excite (Multipl  (None, 18, 27, 960)          0         ['block4h_activation[0][0]',  \n",
            " y)                                                                  'block4h_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4h_project_conv (Conv  (None, 18, 27, 160)          153600    ['block4h_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4h_project_bn (BatchN  (None, 18, 27, 160)          640       ['block4h_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4h_drop (Dropout)      (None, 18, 27, 160)          0         ['block4h_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4h_add (Add)           (None, 18, 27, 160)          0         ['block4h_drop[0][0]',        \n",
            "                                                                     'block4g_add[0][0]']         \n",
            "                                                                                                  \n",
            " block4i_expand_conv (Conv2  (None, 18, 27, 960)          153600    ['block4h_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4i_expand_bn (BatchNo  (None, 18, 27, 960)          3840      ['block4i_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4i_expand_activation   (None, 18, 27, 960)          0         ['block4i_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4i_dwconv (DepthwiseC  (None, 18, 27, 960)          8640      ['block4i_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4i_bn (BatchNormaliza  (None, 18, 27, 960)          3840      ['block4i_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4i_activation (Activa  (None, 18, 27, 960)          0         ['block4i_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4i_se_squeeze (Global  (None, 960)                  0         ['block4i_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4i_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4i_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4i_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4i_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4i_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4i_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4i_se_excite (Multipl  (None, 18, 27, 960)          0         ['block4i_activation[0][0]',  \n",
            " y)                                                                  'block4i_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4i_project_conv (Conv  (None, 18, 27, 160)          153600    ['block4i_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4i_project_bn (BatchN  (None, 18, 27, 160)          640       ['block4i_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4i_drop (Dropout)      (None, 18, 27, 160)          0         ['block4i_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4i_add (Add)           (None, 18, 27, 160)          0         ['block4i_drop[0][0]',        \n",
            "                                                                     'block4h_add[0][0]']         \n",
            "                                                                                                  \n",
            " block4j_expand_conv (Conv2  (None, 18, 27, 960)          153600    ['block4i_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4j_expand_bn (BatchNo  (None, 18, 27, 960)          3840      ['block4j_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4j_expand_activation   (None, 18, 27, 960)          0         ['block4j_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4j_dwconv (DepthwiseC  (None, 18, 27, 960)          8640      ['block4j_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4j_bn (BatchNormaliza  (None, 18, 27, 960)          3840      ['block4j_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4j_activation (Activa  (None, 18, 27, 960)          0         ['block4j_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4j_se_squeeze (Global  (None, 960)                  0         ['block4j_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4j_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block4j_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4j_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block4j_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4j_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block4j_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4j_se_excite (Multipl  (None, 18, 27, 960)          0         ['block4j_activation[0][0]',  \n",
            " y)                                                                  'block4j_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4j_project_conv (Conv  (None, 18, 27, 160)          153600    ['block4j_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4j_project_bn (BatchN  (None, 18, 27, 160)          640       ['block4j_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4j_drop (Dropout)      (None, 18, 27, 160)          0         ['block4j_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4j_add (Add)           (None, 18, 27, 160)          0         ['block4j_drop[0][0]',        \n",
            "                                                                     'block4i_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5a_expand_conv (Conv2  (None, 18, 27, 960)          153600    ['block4j_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5a_expand_bn (BatchNo  (None, 18, 27, 960)          3840      ['block5a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5a_expand_activation   (None, 18, 27, 960)          0         ['block5a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5a_dwconv (DepthwiseC  (None, 18, 27, 960)          24000     ['block5a_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5a_bn (BatchNormaliza  (None, 18, 27, 960)          3840      ['block5a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5a_activation (Activa  (None, 18, 27, 960)          0         ['block5a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5a_se_squeeze (Global  (None, 960)                  0         ['block5a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5a_se_reshape (Reshap  (None, 1, 1, 960)            0         ['block5a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5a_se_reduce (Conv2D)  (None, 1, 1, 40)             38440     ['block5a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5a_se_expand (Conv2D)  (None, 1, 1, 960)            39360     ['block5a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5a_se_excite (Multipl  (None, 18, 27, 960)          0         ['block5a_activation[0][0]',  \n",
            " y)                                                                  'block5a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5a_project_conv (Conv  (None, 18, 27, 224)          215040    ['block5a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5a_project_bn (BatchN  (None, 18, 27, 224)          896       ['block5a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_expand_conv (Conv2  (None, 18, 27, 1344)         301056    ['block5a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5b_expand_bn (BatchNo  (None, 18, 27, 1344)         5376      ['block5b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5b_expand_activation   (None, 18, 27, 1344)         0         ['block5b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5b_dwconv (DepthwiseC  (None, 18, 27, 1344)         33600     ['block5b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5b_bn (BatchNormaliza  (None, 18, 27, 1344)         5376      ['block5b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5b_activation (Activa  (None, 18, 27, 1344)         0         ['block5b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5b_se_squeeze (Global  (None, 1344)                 0         ['block5b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5b_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5b_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5b_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5b_se_excite (Multipl  (None, 18, 27, 1344)         0         ['block5b_activation[0][0]',  \n",
            " y)                                                                  'block5b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5b_project_conv (Conv  (None, 18, 27, 224)          301056    ['block5b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5b_project_bn (BatchN  (None, 18, 27, 224)          896       ['block5b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_drop (Dropout)      (None, 18, 27, 224)          0         ['block5b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5b_add (Add)           (None, 18, 27, 224)          0         ['block5b_drop[0][0]',        \n",
            "                                                                     'block5a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5c_expand_conv (Conv2  (None, 18, 27, 1344)         301056    ['block5b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5c_expand_bn (BatchNo  (None, 18, 27, 1344)         5376      ['block5c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5c_expand_activation   (None, 18, 27, 1344)         0         ['block5c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5c_dwconv (DepthwiseC  (None, 18, 27, 1344)         33600     ['block5c_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5c_bn (BatchNormaliza  (None, 18, 27, 1344)         5376      ['block5c_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5c_activation (Activa  (None, 18, 27, 1344)         0         ['block5c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5c_se_squeeze (Global  (None, 1344)                 0         ['block5c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5c_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5c_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5c_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5c_se_excite (Multipl  (None, 18, 27, 1344)         0         ['block5c_activation[0][0]',  \n",
            " y)                                                                  'block5c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5c_project_conv (Conv  (None, 18, 27, 224)          301056    ['block5c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5c_project_bn (BatchN  (None, 18, 27, 224)          896       ['block5c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5c_drop (Dropout)      (None, 18, 27, 224)          0         ['block5c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5c_add (Add)           (None, 18, 27, 224)          0         ['block5c_drop[0][0]',        \n",
            "                                                                     'block5b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5d_expand_conv (Conv2  (None, 18, 27, 1344)         301056    ['block5c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5d_expand_bn (BatchNo  (None, 18, 27, 1344)         5376      ['block5d_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5d_expand_activation   (None, 18, 27, 1344)         0         ['block5d_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5d_dwconv (DepthwiseC  (None, 18, 27, 1344)         33600     ['block5d_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5d_bn (BatchNormaliza  (None, 18, 27, 1344)         5376      ['block5d_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5d_activation (Activa  (None, 18, 27, 1344)         0         ['block5d_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5d_se_squeeze (Global  (None, 1344)                 0         ['block5d_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5d_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5d_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5d_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5d_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5d_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5d_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5d_se_excite (Multipl  (None, 18, 27, 1344)         0         ['block5d_activation[0][0]',  \n",
            " y)                                                                  'block5d_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5d_project_conv (Conv  (None, 18, 27, 224)          301056    ['block5d_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5d_project_bn (BatchN  (None, 18, 27, 224)          896       ['block5d_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5d_drop (Dropout)      (None, 18, 27, 224)          0         ['block5d_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5d_add (Add)           (None, 18, 27, 224)          0         ['block5d_drop[0][0]',        \n",
            "                                                                     'block5c_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5e_expand_conv (Conv2  (None, 18, 27, 1344)         301056    ['block5d_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5e_expand_bn (BatchNo  (None, 18, 27, 1344)         5376      ['block5e_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5e_expand_activation   (None, 18, 27, 1344)         0         ['block5e_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5e_dwconv (DepthwiseC  (None, 18, 27, 1344)         33600     ['block5e_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5e_bn (BatchNormaliza  (None, 18, 27, 1344)         5376      ['block5e_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5e_activation (Activa  (None, 18, 27, 1344)         0         ['block5e_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5e_se_squeeze (Global  (None, 1344)                 0         ['block5e_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5e_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5e_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5e_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5e_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5e_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5e_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5e_se_excite (Multipl  (None, 18, 27, 1344)         0         ['block5e_activation[0][0]',  \n",
            " y)                                                                  'block5e_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5e_project_conv (Conv  (None, 18, 27, 224)          301056    ['block5e_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5e_project_bn (BatchN  (None, 18, 27, 224)          896       ['block5e_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5e_drop (Dropout)      (None, 18, 27, 224)          0         ['block5e_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5e_add (Add)           (None, 18, 27, 224)          0         ['block5e_drop[0][0]',        \n",
            "                                                                     'block5d_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5f_expand_conv (Conv2  (None, 18, 27, 1344)         301056    ['block5e_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5f_expand_bn (BatchNo  (None, 18, 27, 1344)         5376      ['block5f_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5f_expand_activation   (None, 18, 27, 1344)         0         ['block5f_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5f_dwconv (DepthwiseC  (None, 18, 27, 1344)         33600     ['block5f_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5f_bn (BatchNormaliza  (None, 18, 27, 1344)         5376      ['block5f_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5f_activation (Activa  (None, 18, 27, 1344)         0         ['block5f_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5f_se_squeeze (Global  (None, 1344)                 0         ['block5f_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5f_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5f_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5f_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5f_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5f_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5f_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5f_se_excite (Multipl  (None, 18, 27, 1344)         0         ['block5f_activation[0][0]',  \n",
            " y)                                                                  'block5f_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5f_project_conv (Conv  (None, 18, 27, 224)          301056    ['block5f_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5f_project_bn (BatchN  (None, 18, 27, 224)          896       ['block5f_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5f_drop (Dropout)      (None, 18, 27, 224)          0         ['block5f_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5f_add (Add)           (None, 18, 27, 224)          0         ['block5f_drop[0][0]',        \n",
            "                                                                     'block5e_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5g_expand_conv (Conv2  (None, 18, 27, 1344)         301056    ['block5f_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5g_expand_bn (BatchNo  (None, 18, 27, 1344)         5376      ['block5g_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5g_expand_activation   (None, 18, 27, 1344)         0         ['block5g_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5g_dwconv (DepthwiseC  (None, 18, 27, 1344)         33600     ['block5g_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5g_bn (BatchNormaliza  (None, 18, 27, 1344)         5376      ['block5g_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5g_activation (Activa  (None, 18, 27, 1344)         0         ['block5g_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5g_se_squeeze (Global  (None, 1344)                 0         ['block5g_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5g_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5g_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5g_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5g_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5g_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5g_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5g_se_excite (Multipl  (None, 18, 27, 1344)         0         ['block5g_activation[0][0]',  \n",
            " y)                                                                  'block5g_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5g_project_conv (Conv  (None, 18, 27, 224)          301056    ['block5g_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5g_project_bn (BatchN  (None, 18, 27, 224)          896       ['block5g_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5g_drop (Dropout)      (None, 18, 27, 224)          0         ['block5g_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5g_add (Add)           (None, 18, 27, 224)          0         ['block5g_drop[0][0]',        \n",
            "                                                                     'block5f_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5h_expand_conv (Conv2  (None, 18, 27, 1344)         301056    ['block5g_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5h_expand_bn (BatchNo  (None, 18, 27, 1344)         5376      ['block5h_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5h_expand_activation   (None, 18, 27, 1344)         0         ['block5h_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5h_dwconv (DepthwiseC  (None, 18, 27, 1344)         33600     ['block5h_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5h_bn (BatchNormaliza  (None, 18, 27, 1344)         5376      ['block5h_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5h_activation (Activa  (None, 18, 27, 1344)         0         ['block5h_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5h_se_squeeze (Global  (None, 1344)                 0         ['block5h_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5h_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5h_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5h_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5h_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5h_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5h_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5h_se_excite (Multipl  (None, 18, 27, 1344)         0         ['block5h_activation[0][0]',  \n",
            " y)                                                                  'block5h_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5h_project_conv (Conv  (None, 18, 27, 224)          301056    ['block5h_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5h_project_bn (BatchN  (None, 18, 27, 224)          896       ['block5h_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5h_drop (Dropout)      (None, 18, 27, 224)          0         ['block5h_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5h_add (Add)           (None, 18, 27, 224)          0         ['block5h_drop[0][0]',        \n",
            "                                                                     'block5g_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5i_expand_conv (Conv2  (None, 18, 27, 1344)         301056    ['block5h_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5i_expand_bn (BatchNo  (None, 18, 27, 1344)         5376      ['block5i_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5i_expand_activation   (None, 18, 27, 1344)         0         ['block5i_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5i_dwconv (DepthwiseC  (None, 18, 27, 1344)         33600     ['block5i_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5i_bn (BatchNormaliza  (None, 18, 27, 1344)         5376      ['block5i_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5i_activation (Activa  (None, 18, 27, 1344)         0         ['block5i_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5i_se_squeeze (Global  (None, 1344)                 0         ['block5i_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5i_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5i_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5i_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5i_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5i_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5i_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5i_se_excite (Multipl  (None, 18, 27, 1344)         0         ['block5i_activation[0][0]',  \n",
            " y)                                                                  'block5i_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5i_project_conv (Conv  (None, 18, 27, 224)          301056    ['block5i_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5i_project_bn (BatchN  (None, 18, 27, 224)          896       ['block5i_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5i_drop (Dropout)      (None, 18, 27, 224)          0         ['block5i_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5i_add (Add)           (None, 18, 27, 224)          0         ['block5i_drop[0][0]',        \n",
            "                                                                     'block5h_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5j_expand_conv (Conv2  (None, 18, 27, 1344)         301056    ['block5i_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5j_expand_bn (BatchNo  (None, 18, 27, 1344)         5376      ['block5j_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5j_expand_activation   (None, 18, 27, 1344)         0         ['block5j_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5j_dwconv (DepthwiseC  (None, 18, 27, 1344)         33600     ['block5j_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5j_bn (BatchNormaliza  (None, 18, 27, 1344)         5376      ['block5j_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5j_activation (Activa  (None, 18, 27, 1344)         0         ['block5j_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5j_se_squeeze (Global  (None, 1344)                 0         ['block5j_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5j_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block5j_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5j_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block5j_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5j_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block5j_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5j_se_excite (Multipl  (None, 18, 27, 1344)         0         ['block5j_activation[0][0]',  \n",
            " y)                                                                  'block5j_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5j_project_conv (Conv  (None, 18, 27, 224)          301056    ['block5j_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5j_project_bn (BatchN  (None, 18, 27, 224)          896       ['block5j_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5j_drop (Dropout)      (None, 18, 27, 224)          0         ['block5j_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5j_add (Add)           (None, 18, 27, 224)          0         ['block5j_drop[0][0]',        \n",
            "                                                                     'block5i_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6a_expand_conv (Conv2  (None, 18, 27, 1344)         301056    ['block5j_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6a_expand_bn (BatchNo  (None, 18, 27, 1344)         5376      ['block6a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6a_expand_activation   (None, 18, 27, 1344)         0         ['block6a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6a_dwconv_pad (ZeroPa  (None, 21, 31, 1344)         0         ['block6a_expand_activation[0]\n",
            " dding2D)                                                           [0]']                         \n",
            "                                                                                                  \n",
            " block6a_dwconv (DepthwiseC  (None, 9, 14, 1344)          33600     ['block6a_dwconv_pad[0][0]']  \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block6a_bn (BatchNormaliza  (None, 9, 14, 1344)          5376      ['block6a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6a_activation (Activa  (None, 9, 14, 1344)          0         ['block6a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6a_se_squeeze (Global  (None, 1344)                 0         ['block6a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6a_se_reshape (Reshap  (None, 1, 1, 1344)           0         ['block6a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6a_se_reduce (Conv2D)  (None, 1, 1, 56)             75320     ['block6a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6a_se_expand (Conv2D)  (None, 1, 1, 1344)           76608     ['block6a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6a_se_excite (Multipl  (None, 9, 14, 1344)          0         ['block6a_activation[0][0]',  \n",
            " y)                                                                  'block6a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6a_project_conv (Conv  (None, 9, 14, 384)           516096    ['block6a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6a_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6b_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6b_expand_activation   (None, 9, 14, 2304)          0         ['block6b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6b_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6b_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6b_activation (Activa  (None, 9, 14, 2304)          0         ['block6b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6b_se_squeeze (Global  (None, 2304)                 0         ['block6b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6b_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6b_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6b_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6b_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6b_activation[0][0]',  \n",
            " y)                                                                  'block6b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6b_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6b_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_drop (Dropout)      (None, 9, 14, 384)           0         ['block6b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6b_add (Add)           (None, 9, 14, 384)           0         ['block6b_drop[0][0]',        \n",
            "                                                                     'block6a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6c_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6c_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6c_expand_activation   (None, 9, 14, 2304)          0         ['block6c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6c_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6c_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6c_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6c_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6c_activation (Activa  (None, 9, 14, 2304)          0         ['block6c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6c_se_squeeze (Global  (None, 2304)                 0         ['block6c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6c_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6c_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6c_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6c_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6c_activation[0][0]',  \n",
            " y)                                                                  'block6c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6c_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6c_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6c_drop (Dropout)      (None, 9, 14, 384)           0         ['block6c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6c_add (Add)           (None, 9, 14, 384)           0         ['block6c_drop[0][0]',        \n",
            "                                                                     'block6b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6d_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6d_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6d_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6d_expand_activation   (None, 9, 14, 2304)          0         ['block6d_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6d_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6d_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6d_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6d_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6d_activation (Activa  (None, 9, 14, 2304)          0         ['block6d_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6d_se_squeeze (Global  (None, 2304)                 0         ['block6d_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6d_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6d_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6d_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6d_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6d_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6d_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6d_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6d_activation[0][0]',  \n",
            " y)                                                                  'block6d_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6d_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6d_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6d_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6d_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6d_drop (Dropout)      (None, 9, 14, 384)           0         ['block6d_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6d_add (Add)           (None, 9, 14, 384)           0         ['block6d_drop[0][0]',        \n",
            "                                                                     'block6c_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6e_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6d_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6e_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6e_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6e_expand_activation   (None, 9, 14, 2304)          0         ['block6e_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6e_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6e_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6e_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6e_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6e_activation (Activa  (None, 9, 14, 2304)          0         ['block6e_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6e_se_squeeze (Global  (None, 2304)                 0         ['block6e_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6e_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6e_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6e_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6e_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6e_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6e_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6e_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6e_activation[0][0]',  \n",
            " y)                                                                  'block6e_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6e_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6e_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6e_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6e_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6e_drop (Dropout)      (None, 9, 14, 384)           0         ['block6e_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6e_add (Add)           (None, 9, 14, 384)           0         ['block6e_drop[0][0]',        \n",
            "                                                                     'block6d_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6f_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6e_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6f_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6f_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6f_expand_activation   (None, 9, 14, 2304)          0         ['block6f_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6f_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6f_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6f_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6f_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6f_activation (Activa  (None, 9, 14, 2304)          0         ['block6f_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6f_se_squeeze (Global  (None, 2304)                 0         ['block6f_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6f_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6f_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6f_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6f_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6f_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6f_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6f_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6f_activation[0][0]',  \n",
            " y)                                                                  'block6f_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6f_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6f_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6f_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6f_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6f_drop (Dropout)      (None, 9, 14, 384)           0         ['block6f_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6f_add (Add)           (None, 9, 14, 384)           0         ['block6f_drop[0][0]',        \n",
            "                                                                     'block6e_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6g_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6f_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6g_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6g_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6g_expand_activation   (None, 9, 14, 2304)          0         ['block6g_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6g_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6g_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6g_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6g_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6g_activation (Activa  (None, 9, 14, 2304)          0         ['block6g_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6g_se_squeeze (Global  (None, 2304)                 0         ['block6g_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6g_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6g_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6g_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6g_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6g_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6g_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6g_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6g_activation[0][0]',  \n",
            " y)                                                                  'block6g_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6g_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6g_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6g_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6g_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6g_drop (Dropout)      (None, 9, 14, 384)           0         ['block6g_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6g_add (Add)           (None, 9, 14, 384)           0         ['block6g_drop[0][0]',        \n",
            "                                                                     'block6f_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6h_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6g_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6h_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6h_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6h_expand_activation   (None, 9, 14, 2304)          0         ['block6h_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6h_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6h_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6h_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6h_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6h_activation (Activa  (None, 9, 14, 2304)          0         ['block6h_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6h_se_squeeze (Global  (None, 2304)                 0         ['block6h_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6h_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6h_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6h_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6h_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6h_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6h_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6h_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6h_activation[0][0]',  \n",
            " y)                                                                  'block6h_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6h_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6h_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6h_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6h_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6h_drop (Dropout)      (None, 9, 14, 384)           0         ['block6h_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6h_add (Add)           (None, 9, 14, 384)           0         ['block6h_drop[0][0]',        \n",
            "                                                                     'block6g_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6i_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6h_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6i_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6i_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6i_expand_activation   (None, 9, 14, 2304)          0         ['block6i_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6i_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6i_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6i_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6i_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6i_activation (Activa  (None, 9, 14, 2304)          0         ['block6i_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6i_se_squeeze (Global  (None, 2304)                 0         ['block6i_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6i_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6i_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6i_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6i_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6i_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6i_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6i_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6i_activation[0][0]',  \n",
            " y)                                                                  'block6i_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6i_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6i_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6i_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6i_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6i_drop (Dropout)      (None, 9, 14, 384)           0         ['block6i_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6i_add (Add)           (None, 9, 14, 384)           0         ['block6i_drop[0][0]',        \n",
            "                                                                     'block6h_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6j_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6i_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6j_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6j_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6j_expand_activation   (None, 9, 14, 2304)          0         ['block6j_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6j_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6j_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6j_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6j_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6j_activation (Activa  (None, 9, 14, 2304)          0         ['block6j_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6j_se_squeeze (Global  (None, 2304)                 0         ['block6j_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6j_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6j_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6j_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6j_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6j_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6j_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6j_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6j_activation[0][0]',  \n",
            " y)                                                                  'block6j_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6j_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6j_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6j_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6j_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6j_drop (Dropout)      (None, 9, 14, 384)           0         ['block6j_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6j_add (Add)           (None, 9, 14, 384)           0         ['block6j_drop[0][0]',        \n",
            "                                                                     'block6i_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6k_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6j_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6k_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6k_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6k_expand_activation   (None, 9, 14, 2304)          0         ['block6k_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6k_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6k_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6k_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6k_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6k_activation (Activa  (None, 9, 14, 2304)          0         ['block6k_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6k_se_squeeze (Global  (None, 2304)                 0         ['block6k_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6k_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6k_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6k_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6k_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6k_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6k_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6k_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6k_activation[0][0]',  \n",
            " y)                                                                  'block6k_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6k_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6k_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6k_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6k_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6k_drop (Dropout)      (None, 9, 14, 384)           0         ['block6k_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6k_add (Add)           (None, 9, 14, 384)           0         ['block6k_drop[0][0]',        \n",
            "                                                                     'block6j_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6l_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6k_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6l_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6l_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6l_expand_activation   (None, 9, 14, 2304)          0         ['block6l_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6l_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6l_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6l_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6l_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6l_activation (Activa  (None, 9, 14, 2304)          0         ['block6l_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6l_se_squeeze (Global  (None, 2304)                 0         ['block6l_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6l_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6l_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6l_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6l_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6l_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6l_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6l_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6l_activation[0][0]',  \n",
            " y)                                                                  'block6l_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6l_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6l_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6l_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6l_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6l_drop (Dropout)      (None, 9, 14, 384)           0         ['block6l_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6l_add (Add)           (None, 9, 14, 384)           0         ['block6l_drop[0][0]',        \n",
            "                                                                     'block6k_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6m_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6l_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6m_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block6m_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6m_expand_activation   (None, 9, 14, 2304)          0         ['block6m_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6m_dwconv (DepthwiseC  (None, 9, 14, 2304)          57600     ['block6m_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6m_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block6m_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6m_activation (Activa  (None, 9, 14, 2304)          0         ['block6m_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6m_se_squeeze (Global  (None, 2304)                 0         ['block6m_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6m_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block6m_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6m_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block6m_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6m_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block6m_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6m_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block6m_activation[0][0]',  \n",
            " y)                                                                  'block6m_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6m_project_conv (Conv  (None, 9, 14, 384)           884736    ['block6m_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6m_project_bn (BatchN  (None, 9, 14, 384)           1536      ['block6m_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6m_drop (Dropout)      (None, 9, 14, 384)           0         ['block6m_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6m_add (Add)           (None, 9, 14, 384)           0         ['block6m_drop[0][0]',        \n",
            "                                                                     'block6l_add[0][0]']         \n",
            "                                                                                                  \n",
            " block7a_expand_conv (Conv2  (None, 9, 14, 2304)          884736    ['block6m_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block7a_expand_bn (BatchNo  (None, 9, 14, 2304)          9216      ['block7a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block7a_expand_activation   (None, 9, 14, 2304)          0         ['block7a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block7a_dwconv (DepthwiseC  (None, 9, 14, 2304)          20736     ['block7a_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block7a_bn (BatchNormaliza  (None, 9, 14, 2304)          9216      ['block7a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block7a_activation (Activa  (None, 9, 14, 2304)          0         ['block7a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block7a_se_squeeze (Global  (None, 2304)                 0         ['block7a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block7a_se_reshape (Reshap  (None, 1, 1, 2304)           0         ['block7a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block7a_se_reduce (Conv2D)  (None, 1, 1, 96)             221280    ['block7a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block7a_se_expand (Conv2D)  (None, 1, 1, 2304)           223488    ['block7a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block7a_se_excite (Multipl  (None, 9, 14, 2304)          0         ['block7a_activation[0][0]',  \n",
            " y)                                                                  'block7a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block7a_project_conv (Conv  (None, 9, 14, 640)           1474560   ['block7a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block7a_project_bn (BatchN  (None, 9, 14, 640)           2560      ['block7a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block7b_expand_conv (Conv2  (None, 9, 14, 3840)          2457600   ['block7a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block7b_expand_bn (BatchNo  (None, 9, 14, 3840)          15360     ['block7b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block7b_expand_activation   (None, 9, 14, 3840)          0         ['block7b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block7b_dwconv (DepthwiseC  (None, 9, 14, 3840)          34560     ['block7b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block7b_bn (BatchNormaliza  (None, 9, 14, 3840)          15360     ['block7b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block7b_activation (Activa  (None, 9, 14, 3840)          0         ['block7b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block7b_se_squeeze (Global  (None, 3840)                 0         ['block7b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block7b_se_reshape (Reshap  (None, 1, 1, 3840)           0         ['block7b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block7b_se_reduce (Conv2D)  (None, 1, 1, 160)            614560    ['block7b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block7b_se_expand (Conv2D)  (None, 1, 1, 3840)           618240    ['block7b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block7b_se_excite (Multipl  (None, 9, 14, 3840)          0         ['block7b_activation[0][0]',  \n",
            " y)                                                                  'block7b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block7b_project_conv (Conv  (None, 9, 14, 640)           2457600   ['block7b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block7b_project_bn (BatchN  (None, 9, 14, 640)           2560      ['block7b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block7b_drop (Dropout)      (None, 9, 14, 640)           0         ['block7b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block7b_add (Add)           (None, 9, 14, 640)           0         ['block7b_drop[0][0]',        \n",
            "                                                                     'block7a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block7c_expand_conv (Conv2  (None, 9, 14, 3840)          2457600   ['block7b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block7c_expand_bn (BatchNo  (None, 9, 14, 3840)          15360     ['block7c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block7c_expand_activation   (None, 9, 14, 3840)          0         ['block7c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block7c_dwconv (DepthwiseC  (None, 9, 14, 3840)          34560     ['block7c_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block7c_bn (BatchNormaliza  (None, 9, 14, 3840)          15360     ['block7c_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block7c_activation (Activa  (None, 9, 14, 3840)          0         ['block7c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block7c_se_squeeze (Global  (None, 3840)                 0         ['block7c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block7c_se_reshape (Reshap  (None, 1, 1, 3840)           0         ['block7c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block7c_se_reduce (Conv2D)  (None, 1, 1, 160)            614560    ['block7c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block7c_se_expand (Conv2D)  (None, 1, 1, 3840)           618240    ['block7c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block7c_se_excite (Multipl  (None, 9, 14, 3840)          0         ['block7c_activation[0][0]',  \n",
            " y)                                                                  'block7c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block7c_project_conv (Conv  (None, 9, 14, 640)           2457600   ['block7c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block7c_project_bn (BatchN  (None, 9, 14, 640)           2560      ['block7c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block7c_drop (Dropout)      (None, 9, 14, 640)           0         ['block7c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block7c_add (Add)           (None, 9, 14, 640)           0         ['block7c_drop[0][0]',        \n",
            "                                                                     'block7b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block7d_expand_conv (Conv2  (None, 9, 14, 3840)          2457600   ['block7c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block7d_expand_bn (BatchNo  (None, 9, 14, 3840)          15360     ['block7d_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block7d_expand_activation   (None, 9, 14, 3840)          0         ['block7d_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block7d_dwconv (DepthwiseC  (None, 9, 14, 3840)          34560     ['block7d_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block7d_bn (BatchNormaliza  (None, 9, 14, 3840)          15360     ['block7d_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block7d_activation (Activa  (None, 9, 14, 3840)          0         ['block7d_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block7d_se_squeeze (Global  (None, 3840)                 0         ['block7d_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block7d_se_reshape (Reshap  (None, 1, 1, 3840)           0         ['block7d_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block7d_se_reduce (Conv2D)  (None, 1, 1, 160)            614560    ['block7d_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block7d_se_expand (Conv2D)  (None, 1, 1, 3840)           618240    ['block7d_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block7d_se_excite (Multipl  (None, 9, 14, 3840)          0         ['block7d_activation[0][0]',  \n",
            " y)                                                                  'block7d_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block7d_project_conv (Conv  (None, 9, 14, 640)           2457600   ['block7d_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block7d_project_bn (BatchN  (None, 9, 14, 640)           2560      ['block7d_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block7d_drop (Dropout)      (None, 9, 14, 640)           0         ['block7d_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block7d_add (Add)           (None, 9, 14, 640)           0         ['block7d_drop[0][0]',        \n",
            "                                                                     'block7c_add[0][0]']         \n",
            "                                                                                                  \n",
            " top_conv (Conv2D)           (None, 9, 14, 2560)          1638400   ['block7d_add[0][0]']         \n",
            "                                                                                                  \n",
            " top_bn (BatchNormalization  (None, 9, 14, 2560)          10240     ['top_conv[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " top_activation (Activation  (None, 9, 14, 2560)          0         ['top_bn[0][0]']              \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " global_average_pooling2d (  (None, 2560)                 0         ['top_activation[0][0]']      \n",
            " GlobalAveragePooling2D)                                                                          \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 128)                  327808    ['global_average_pooling2d[0][\n",
            "                                                                    0]']                          \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 2)                    258       ['dense[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 64425753 (245.76 MB)\n",
            "Trainable params: 64115026 (244.58 MB)\n",
            "Non-trainable params: 310727 (1.19 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras import models, layers, optimizers, regularizers, callbacks, metrics\n",
        "\n",
        "# base_model = efn(input_shape=input_shape, weights='imagenet', include_top=False)\n",
        "# base_model.trainable = False # (transfer learning)\n",
        "base_model = efn\n",
        "x = base_model.output\n",
        "x = layers.GlobalAveragePooling2D()(x)\n",
        "x = layers.Dense(128)(x)\n",
        "out = layers.Dense(num_classes, activation=\"softmax\")(x)\n",
        "\n",
        "model = models.Model(inputs=base_model.input, outputs=out)\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "-KuQnSbKGFC_"
      },
      "outputs": [],
      "source": [
        "# compile model\n",
        "model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NU4rgntj3T8S"
      },
      "outputs": [],
      "source": [
        "#model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=[metrics.AUC(from_logits=True)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bYjMIdBxzFq8"
      },
      "outputs": [],
      "source": [
        "## set Checkpoint : save best only, verbose on\n",
        "#checkpoint = callbacks.ModelCheckpoint(\"railtrack_enetB7.hdf5\", monitor='val_accuracy', verbose=1, save_best_only=True, mode='auto', save_freq=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "h4Z9qdjyGHpw"
      },
      "outputs": [],
      "source": [
        "STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\n",
        "STEP_SIZE_VAL  =val_generator.n//val_generator.batch_size\n",
        "STEP_SIZE_TEST =test_generator.n//test_generator.batch_size\n",
        "num_epochs = 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rzvp6ivlGJlW",
        "outputId": "352b588a-ca52-4229-ffa6-9214382a50e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2025-03-17 15:59:31.908012: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "90/90 [==============================] - ETA: 0s - loss: 0.9815 - accuracy: 0.4868"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2025-03-17 16:03:26.145157: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "90/90 [==============================] - 251s 2s/step - loss: 0.9815 - accuracy: 0.4868 - val_loss: 0.8947 - val_accuracy: 0.4886\n",
            "Epoch 2/5\n",
            "90/90 [==============================] - 204s 2s/step - loss: 0.7555 - accuracy: 0.5431 - val_loss: 0.6691 - val_accuracy: 0.4886\n",
            "Epoch 3/5\n",
            "90/90 [==============================] - 196s 2s/step - loss: 0.6777 - accuracy: 0.5556 - val_loss: 0.7488 - val_accuracy: 0.4886\n",
            "Epoch 4/5\n",
            "90/90 [==============================] - 199s 2s/step - loss: 0.7225 - accuracy: 0.5132 - val_loss: 0.7291 - val_accuracy: 0.5114\n",
            "Epoch 5/5\n",
            "90/90 [==============================] - 204s 2s/step - loss: 0.7058 - accuracy: 0.5090 - val_loss: 0.6943 - val_accuracy: 0.5114\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_generator,steps_per_epoch=STEP_SIZE_TRAIN,epochs=num_epochs, validation_data=val_generator, validation_steps=STEP_SIZE_VAL) #callbacks=[checkpoint])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/liubin/miniforge3/envs/LD_Env/lib/python3.9/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ],
      "source": [
        "\n",
        "model.save(\"Model250317.h5\", include_optimizer=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QrA0rvCzRXP",
        "outputId": "78797aa2-0d8f-41f6-a7ee-8091cba8b871"
      },
      "outputs": [],
      "source": [
        "\n",
        "# tf.keras.models.save_model(model,'/Users/binliu/09.机器学习示例/08.Bearing Fault Detection using SDP/model1.h5')\n",
        "#model.load_weights(\"model name here\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GfoLCnqtJbX8",
        "outputId": "47133d7a-4a24-43ea-dce5-f7666dee2180"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2025-03-17 16:25:06.565979: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "12/12 [==============================] - 12s 600ms/step\n",
            "[[89  1]\n",
            " [90  0]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "predY=model.predict(test_generator) \n",
        "y_pred = np.argmax(predY,axis=1)\n",
        "y_actual = test_generator.classes\n",
        "cm = confusion_matrix(y_actual, y_pred)\n",
        "print(cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HeTfPL3AJkaN",
        "outputId": "1bc32cb5-b185-40f6-d6cb-8b0adad507f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Inner       0.50      0.99      0.66        90\n",
            "       Outer       0.00      0.00      0.00        90\n",
            "\n",
            "    accuracy                           0.49       180\n",
            "   macro avg       0.25      0.49      0.33       180\n",
            "weighted avg       0.25      0.49      0.33       180\n",
            "\n"
          ]
        }
      ],
      "source": [
        "labels = ['Inner', 'Outer']\n",
        "print(classification_report(y_actual, y_pred, target_names=labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "yr7PdmuuUWFk",
        "outputId": "93a1656a-e8aa-478b-97c0-076bd73f0429"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "keys=history.history.keys()\n",
        "print(keys)\n",
        "\n",
        "def show_train_history(hisData,train,val): \n",
        "    plt.plot(hisData.history[train])\n",
        "    plt.plot(hisData.history[val])\n",
        "    plt.title('Training History')\n",
        "    plt.ylabel(train)\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend(['train', 'val'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "show_train_history(history, 'loss', 'val_loss')\n",
        "show_train_history(history, 'accuracy', 'val_accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {},
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "from keras.models import load_model\n",
        "def predictImage(filename):\n",
        "    img1 = image.load_img(filename,target_size=(288,432))\n",
        "    \n",
        "    plt.imshow(img1)\n",
        " \n",
        "    ImagIn = image.img_to_array(img1)\n",
        "    \n",
        "    Result = np.expand_dims(ImagIn,axis=0)\n",
        "    load_model(\"Model250317.h5\")\n",
        "    val = model.predict(Result)\n",
        "\n",
        "    if val[0,0] >= 0.5:\n",
        "        \n",
        "        plt.xlabel(\"Inner\",fontsize=30)\n",
        "        \n",
        "    \n",
        "    elif val[0,0]<0.5:\n",
        "        \n",
        "        plt.xlabel(\"Outer\",fontsize=30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 63ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "predictImage('./48kpart2/Inner/48kIR_39,3_27.png')"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "include_colab_link": true,
      "name": "Minor Project Review 2",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "LD_Env",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
